Mineralocorticoid receptors dampen glucocorticoid receptor sensitivity to stress via regulation of FKBP5

Responding to different dynamic levels of stress is critical for mammalian survival. Disruption of mineralocorticoid receptor (MR) and glucocorticoid receptor (GR) signaling is proposed to underlie hypothalamic-pituitary-adrenal (HPA) axis dysregulation observed in stress-related psychiatric disorders. In this study, we show that FK506-binding protein 51 (FKBP5) plays a critical role in fine-tuning MR:GR balance in the hippocampus. Biotinylated-oligonucleotide immunoprecipitation in primary hippocampal neurons reveals that MR binding, rather than GR binding, to the Fkbp5 gene regulates FKBP5 expression during baseline activity of glucocorticoids. Notably, FKBP5 andMR exhibit similar hippocampal expression patterns in mice and humans, which are distinct from that of the GR. Pharmacological inhibition and region- and cell type-specific receptor deletion in mice further demonstrate that lack of MR decreases hippocampal Fkbp5 levels and dampens the stress-induced increase in glucocorticoid levels. Overall, our findings demonstrate that MR-dependent changes in baseline Fkbp5 expression modify GR sensitivity to glucocorticoids, providing insight into mechanisms of stress homeostasis.Diabetes mellitus: pathophysiological changes and therap

Fine-mapping genomic loci refines bipolar disorder risk genes

Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI)

Optimization techniques for multivariate least trimmed absolute deviation estimation

Given a dataset an outlier can be defined as an observation that does not follow the statistical properties of the majority of the data. Computation of the location estimate is of fundamental importance in data analysis, and it is well known in statistics that classical methods, such as taking the sample average, can be greatly affected by the presence of outliers in the data. Using the median instead of the mean can partially resolve this issue but not completely. For the univariate case, a robust version of the median is the Least Trimmed Absolute Deviation (LTAD) robust estimator introduced in Tableman (Stat Probab Lett 19(5):387–398, 1994), which has desirable asymptotic properties such as robustness, consistently, high breakdown and normality. There are different generalizations of the LTAD for multivariate data, depending on the choice of norm. Chatzinakos et al. (J Comb Optim, 2015) we present such a generalization using the Euclidean norm and propose a solution technique for the resulting combinatorial optimization problem, based on a necessary condition, that results in a highly convergent local search algorithm. In this subsequent work, we use the L1 norm to generalize the LTAD to higher dimensions, and show that the resulting mixed integer programming problem has an integral relaxation, after applying an appropriate data transformation. Moreover, we utilize the structure of the problem to show that the resulting LP’s can be solved efficiently using a subgradient optimization approach. The robust statistical properties of the proposed estimator are verified by extensive computational results

Intergenerational trauma is associated with expression alterations in glucocorticoid- and immune-related genes

Offspring of trauma survivors are more likely to develop PTSD, mood, and anxiety disorders and demonstrate endocrine and molecular alterations compared to controls. This study reports the association between parental Holocaust exposure and genome-wide gene expression in peripheral blood mononuclear cells (PBMC) from 77 Holocaust survivor offspring and 15 comparison subjects. Forty-two differentially expressed genes (DEGs) were identified in association with parental Holocaust exposure (FDR-adjusted p < 0.05); most of these genes were downregulated and co-expressed in a gene network related to immune cell functions. When both parental Holocaust exposure and maternal age at Holocaust exposure shared DEGs, fold changes were in the opposite direction. Similarly, fold changes of shared DEGs associated with maternal PTSD and paternal PTSD were in opposite directions, while fold changes of shared DEGs associated with both maternal and paternal Holocaust exposure or associated with both maternal and paternal age at Holocaust exposure were in the same direction. Moreover, the DEGs associated with parental Holocaust exposure were enriched for glucocorticoid-regulated genes and immune pathways with some of these genes mediating the effects of parental Holocaust exposure on C-reactive protein. The top gene across all analyses was MMP8, encoding the matrix metalloproteinase 8, which is a regulator of innate immunity. To conclude, this study identified a set of glucocorticoid and immune-related genes in association with parental Holocaust exposure with differential effects based on parental exposure-related factors

Increasing the resolution and precision of psychiatric genome-wide association studies by re-imputing summary statistics using a large, diverse reference panel.

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary-statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel for example, ~2,500-subject coming from the 1000 Genome-Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: (a) uses a much larger and more diverse reference panel compared to traditional reference panels, and (b) can estimate weights of ethnic-mixture based solely on Z-scores, when allele frequencies are not available. We applied DISTMIX2 to GWAS summary-statistics from the psychiatric genetic consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder GWAS found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most psychiatric GWAS-studies

TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as-is to ethnically mixed-cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example,MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment